Overview

Dataset statistics

Number of variables23
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory264.3 KiB
Average record size in memory184.1 B

Variable types

Categorical16
Numeric7

Alerts

Ratio_Gain_Effort is highly overall correlated with revenu_mensuelHigh correlation
nombre_participation_pee is highly overall correlated with statut_maritalHigh correlation
nouveau_responsable is highly overall correlated with ratio_stagnation_posteHigh correlation
ratio_stagnation_poste is highly overall correlated with nouveau_responsableHigh correlation
revenu_mensuel is highly overall correlated with Ratio_Gain_EffortHigh correlation
statut_marital is highly overall correlated with nombre_participation_peeHigh correlation
nb_formations_suivies has 54 (3.7%) zerosZeros
Ratio_Stagnation_entreprise has 581 (39.5%) zerosZeros
ratio_stagnation_poste has 244 (16.6%) zerosZeros

Reproduction

Analysis started2026-01-22 16:07:25.482390
Analysis finished2026-01-22 16:07:33.549728
Duration8.07 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2026-01-22T17:07:33.624950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:33.718147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2026-01-22T17:07:33.826944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:33.910108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2026-01-22T17:07:34.013064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:34.094882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2026-01-22T17:07:34.202587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:34.284912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2026-01-22T17:07:34.390716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:34.472278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1054 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Length

2026-01-22T17:07:34.575554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:34.649291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%
Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:34.716951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2026-01-22T17:07:34.804659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

genre
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
882 
1
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Length

2026-01-22T17:07:34.915977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:34.989067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring characters

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

revenu_mensuel
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:35.081294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2026-01-22T17:07:35.226136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
34523
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
63473
 
0.2%
23803
 
0.2%
27413
 
0.2%
55623
 
0.2%
24043
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

statut_marital
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Marié(e)
673 
Célibataire
470 
Divorcé(e)
327 

Length

Max length11
Median length10
Mean length9.4040816
Min length8

Characters and Unicode

Total characters13824
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCélibataire
2nd rowMarié(e)
3rd rowCélibataire
4th rowMarié(e)
5th rowMarié(e)

Common Values

ValueCountFrequency (%)
Marié(e)673
45.8%
Célibataire470
32.0%
Divorcé(e)327
22.2%

Length

2026-01-22T17:07:35.351568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:35.422182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
marié(e673
45.8%
célibataire470
32.0%
divorcé(e327
22.2%

Most occurring characters

ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

poste
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Cadre Commercial
326 
Assistant de Direction
292 
Consultant
259 
Tech Lead
145 
Manager
131 
Other values (4)
317 

Length

Max length23
Median length19
Mean length15.168027
Min length7

Characters and Unicode

Total characters22297
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCadre Commercial
2nd rowAssistant de Direction
3rd rowConsultant
4th rowAssistant de Direction
5th rowConsultant

Common Values

ValueCountFrequency (%)
Cadre Commercial326
22.2%
Assistant de Direction292
19.9%
Consultant259
17.6%
Tech Lead145
9.9%
Manager131
8.9%
Senior Manager102
 
6.9%
Représentant Commercial83
 
5.6%
Directeur Technique80
 
5.4%
Ressources Humaines52
 
3.5%

Length

2026-01-22T17:07:35.519924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:35.619979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
commercial409
14.4%
cadre326
11.5%
assistant292
10.3%
de292
10.3%
direction292
10.3%
consultant259
9.1%
manager233
8.2%
tech145
 
5.1%
lead145
 
5.1%
senior102
 
3.6%
Other values (5)347
12.2%

Most occurring characters

ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Length

2026-01-22T17:07:35.769646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:35.846768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

nombre_participation_pee
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2026-01-22T17:07:36.200235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:36.283532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

nb_formations_suivies
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:36.355233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2026-01-22T17:07:36.439036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

niveau_education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2026-01-22T17:07:36.552584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:36.643287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Occasionnel
1043 
Frequent
277 
Aucun
150 

Length

Max length11
Median length11
Mean length9.822449
Min length5

Characters and Unicode

Total characters14439
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOccasionnel
2nd rowFrequent
3rd rowOccasionnel
4th rowFrequent
5th rowOccasionnel

Common Values

ValueCountFrequency (%)
Occasionnel1043
71.0%
Frequent277
 
18.8%
Aucun150
 
10.2%

Length

2026-01-22T17:07:36.752190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:36.824310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
occasionnel1043
71.0%
frequent277
 
18.8%
aucun150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Ratio_Stagnation_entreprise
Real number (ℝ)

Zeros 

Distinct113
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29023166
Minimum0
Maximum1
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:36.934816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.16666667
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.34052096
Coefficient of variation (CV)1.173273
Kurtosis-0.45861468
Mean0.29023166
Median Absolute Deviation (MAD)0.16666667
Skewness0.96010985
Sum426.64054
Variance0.11595453
MonotonicityNot monotonic
2026-01-22T17:07:37.079566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0581
39.5%
1135
 
9.2%
0.283
 
5.6%
0.333333333376
 
5.2%
0.2556
 
3.8%
0.166666666739
 
2.7%
0.536
 
2.4%
0.142857142928
 
1.9%
0.87527
 
1.8%
0.124
 
1.6%
Other values (103)385
26.2%
ValueCountFrequency (%)
0581
39.5%
0.027027027031
 
0.1%
0.029411764711
 
0.1%
0.03030303031
 
0.1%
0.038461538461
 
0.1%
0.041666666672
 
0.1%
0.045454545451
 
0.1%
0.047619047621
 
0.1%
0.052
 
0.1%
0.052631578951
 
0.1%
ValueCountFrequency (%)
1135
9.2%
0.92307692311
 
0.1%
0.91666666672
 
0.1%
0.90909090911
 
0.1%
0.911
 
0.7%
0.88888888895
 
0.3%
0.88235294122
 
0.1%
0.87527
 
1.8%
0.85714285713
 
0.2%
0.84615384621
 
0.1%

match_etudes_poste
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1023 
0
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11023
69.6%
0447
30.4%

Length

2026-01-22T17:07:37.213164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:37.290074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11023
69.6%
0447
30.4%

Most occurring characters

ValueCountFrequency (%)
11023
69.6%
0447
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11023
69.6%
0447
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11023
69.6%
0447
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11023
69.6%
0447
30.4%

duree_moyenne_par_poste
Real number (ℝ)

Distinct178
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1934778
Minimum0
Maximum38
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:37.383734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.6
median3
Q35
95-th percentile10.5
Maximum38
Range38
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation4.0355036
Coefficient of variation (CV)0.9623286
Kurtosis14.201804
Mean4.1934778
Median Absolute Deviation (MAD)1.8333333
Skewness2.9597957
Sum6164.4123
Variance16.285289
MonotonicityNot monotonic
2026-01-22T17:07:37.521379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5123
 
8.4%
385
 
5.8%
0.583
 
5.6%
170
 
4.8%
268
 
4.6%
2.565
 
4.4%
465
 
4.4%
650
 
3.4%
1039
 
2.7%
1.536
 
2.4%
Other values (168)786
53.5%
ValueCountFrequency (%)
011
 
0.7%
0.32
 
0.1%
0.33333333331
 
0.1%
0.3751
 
0.1%
0.43
 
0.2%
0.42857142861
 
0.1%
0.44444444443
 
0.2%
0.583
5.6%
0.55555555561
 
0.1%
0.57142857141
 
0.1%
ValueCountFrequency (%)
381
 
0.1%
371
 
0.1%
342
 
0.1%
281
 
0.1%
251
 
0.1%
233
0.2%
222
 
0.1%
217
0.5%
203
0.2%
192
 
0.1%

habite_loin
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1193 
1
277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Length

2026-01-22T17:07:37.651771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:37.724707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring characters

ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

ratio_stagnation_poste
Real number (ℝ)

High correlation  Zeros 

Distinct119
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48070114
Minimum0
Maximum0.88235294
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:37.823416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.33333333
median0.5
Q30.66666667
95-th percentile0.85714286
Maximum0.88235294
Range0.88235294
Interquartile range (IQR)0.33333333

Descriptive statistics

Standard deviation0.2741283
Coefficient of variation (CV)0.57026764
Kurtosis-0.84269854
Mean0.48070114
Median Absolute Deviation (MAD)0.16666667
Skewness-0.55930822
Sum706.63068
Variance0.075146327
MonotonicityNot monotonic
2026-01-22T17:07:37.958575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0244
16.6%
0.5201
13.7%
0.6666666667200
13.6%
0.333333333378
 
5.3%
0.87567
 
4.6%
0.659
 
4.0%
0.777777777858
 
3.9%
0.456
 
3.8%
0.636363636434
 
2.3%
0.833
 
2.2%
Other values (109)440
29.9%
ValueCountFrequency (%)
0244
16.6%
0.06251
 
0.1%
0.066666666671
 
0.1%
0.086956521741
 
0.1%
0.090909090911
 
0.1%
0.12
 
0.1%
0.10714285711
 
0.1%
0.11111111112
 
0.1%
0.1254
 
0.3%
0.13043478261
 
0.1%
ValueCountFrequency (%)
0.88235294121
 
0.1%
0.87567
4.6%
0.86666666672
 
0.1%
0.85714285716
 
0.4%
0.851
 
0.1%
0.84615384624
 
0.3%
0.84210526323
 
0.2%
0.83333333338
 
0.5%
0.82352941181
 
0.1%
0.818181818231
2.1%

nouveau_responsable
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1131 
1
339 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Length

2026-01-22T17:07:38.085824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T17:07:38.158219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Most occurring characters

ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01131
76.9%
1339
 
23.1%

Ratio_Gain_Effort
Real number (ℝ)

High correlation 

Distinct1380
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5576.334
Minimum504.5
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-22T17:07:38.254108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum504.5
5-th percentile1226.375
Q12520
median4231.5
Q36811.75
95-th percentile16871.95
Maximum19999
Range19494.5
Interquartile range (IQR)4291.75

Descriptive statistics

Standard deviation4430.098
Coefficient of variation (CV)0.79444631
Kurtosis2.087355
Mean5576.334
Median Absolute Deviation (MAD)1925.5
Skewness1.6102611
Sum8197211
Variance19625768
MonotonicityNot monotonic
2026-01-22T17:07:38.403265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26104
 
0.3%
22693
 
0.2%
20073
 
0.2%
46393
 
0.2%
24513
 
0.2%
34523
 
0.2%
27053
 
0.2%
27203
 
0.2%
32912
 
0.1%
49682
 
0.1%
Other values (1370)1441
98.0%
ValueCountFrequency (%)
504.51
0.1%
5591
0.1%
564.51
0.1%
784.51
0.1%
800.51
0.1%
837.51
0.1%
8511
0.1%
929.51
0.1%
9391
0.1%
10111
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%
196651
0.1%
196581
0.1%

Interactions

2026-01-22T17:07:32.300642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.334899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.207991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.069912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.096713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.856380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.601493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.418829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.458962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.319281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.372146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.239297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.962904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.701633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.532567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.597016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.445275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.517030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.347091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.073166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.804432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.645054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.712469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.574129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.635484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.449239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.182237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.901210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.759171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.834946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.689714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.742420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.550004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.285134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.996188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.877411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:27.993017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.802706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.851589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.654223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.389053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.100342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.990086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.097063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:28.939975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:29.968324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:30.748035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:31.488054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T17:07:32.191971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-22T17:07:38.535405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Ratio_Gain_EffortRatio_Stagnation_entreprisea_quitte_l_entrepriseaugementation_salaire_precedenteduree_moyenne_par_postefrequence_deplacementgenrehabite_loinheure_supplementairesmatch_etudes_postenb_formations_suiviesniveau_educationnombre_participation_peenote_evaluation_precedentenouveau_responsableposteratio_stagnation_posterevenu_mensuelsatisfaction_employee_environnementsatisfaction_employee_equilibre_pro_persosatisfaction_employee_equipesatisfaction_employee_nature_travailstatut_marital
Ratio_Gain_Effort1.0000.1020.258-0.0260.4460.0000.0000.0430.4020.039-0.0020.0860.0240.0000.1750.3620.1480.9030.0310.0000.0000.0000.000
Ratio_Stagnation_entreprise0.1021.0000.097-0.0430.1460.0100.0260.0000.0000.0700.0060.0470.0000.0330.3240.0620.3250.1060.0000.0360.0300.0150.000
a_quitte_l_entreprise0.2580.0971.0000.0000.1390.1230.0090.0440.2430.0000.0790.0000.1980.1320.1780.2310.1880.2170.1150.0950.0390.0990.173
augementation_salaire_precedente-0.026-0.0430.0001.000-0.0190.0300.0490.0000.0000.000-0.0040.0210.0000.0360.0000.000-0.006-0.0340.0000.0000.0270.0000.000
duree_moyenne_par_poste0.4460.1460.139-0.0191.0000.0000.0700.0760.0000.0000.0170.0410.0080.0270.2300.1330.2310.4930.0150.0000.0430.0000.043
frequence_deplacement0.0000.0100.1230.0300.0001.0000.0370.0240.0240.0000.0000.0000.0000.0160.0000.0000.0480.0250.0000.0000.0000.0000.035
genre0.0000.0260.0090.0490.0700.0371.0000.0000.0310.0000.0000.0000.0000.0000.0000.0740.0610.0460.0000.0000.0000.0000.032
habite_loin0.0430.0000.0440.0000.0760.0240.0001.0000.0000.0000.0340.0000.0590.0120.0000.0000.0000.0740.0000.0000.0000.0000.028
heure_supplementaires0.4020.0000.2430.0000.0000.0240.0310.0001.0000.0140.0990.0010.0000.0000.0000.0000.0000.0000.0600.0000.0250.0220.000
match_etudes_poste0.0390.0700.0000.0000.0000.0000.0000.0000.0141.0000.0000.0450.0220.0000.0000.3970.0200.0750.0000.0460.0000.0000.022
nb_formations_suivies-0.0020.0060.079-0.0040.0170.0000.0000.0340.0990.0001.0000.0270.0000.0130.0000.000-0.024-0.0350.0000.0000.0000.0210.000
niveau_education0.0860.0470.0000.0210.0410.0000.0000.0000.0010.0450.0271.0000.0270.0000.0000.0510.0410.0940.0190.0000.0160.0150.000
nombre_participation_pee0.0240.0000.1980.0000.0080.0000.0000.0590.0000.0220.0000.0271.0000.0220.0630.0390.0610.0560.0000.0190.0300.0000.581
note_evaluation_precedente0.0000.0330.1320.0360.0270.0160.0000.0120.0000.0000.0130.0000.0221.0000.0690.0000.0440.0460.0340.0000.0000.0000.024
nouveau_responsable0.1750.3240.1780.0000.2300.0000.0000.0000.0000.0000.0000.0000.0630.0691.0000.1610.6220.2090.0000.0000.0080.0000.059
poste0.3620.0620.2310.0000.1330.0000.0740.0000.0000.3970.0000.0510.0390.0000.1611.0000.0910.4230.0000.0290.0300.0000.061
ratio_stagnation_poste0.1480.3250.188-0.0060.2310.0480.0610.0000.0000.020-0.0240.0410.0610.0440.6220.0911.0000.1540.0300.0560.0000.0000.050
revenu_mensuel0.9030.1060.217-0.0340.4930.0250.0460.0740.0000.075-0.0350.0940.0560.0460.2090.4230.1541.0000.0000.0000.0430.0000.061
satisfaction_employee_environnement0.0310.0000.1150.0000.0150.0000.0000.0000.0600.0000.0000.0190.0000.0340.0000.0000.0300.0001.0000.0000.0000.0000.019
satisfaction_employee_equilibre_pro_perso0.0000.0360.0950.0000.0000.0000.0000.0000.0000.0460.0000.0000.0190.0000.0000.0290.0560.0000.0001.0000.0000.0000.000
satisfaction_employee_equipe0.0000.0300.0390.0270.0430.0000.0000.0000.0250.0000.0000.0160.0300.0000.0080.0300.0000.0430.0000.0001.0000.0000.025
satisfaction_employee_nature_travail0.0000.0150.0990.0000.0000.0000.0000.0000.0220.0000.0210.0150.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
statut_marital0.0000.0000.1730.0000.0430.0350.0320.0280.0000.0220.0000.0000.5810.0240.0590.0610.0500.0610.0190.0000.0250.0001.000

Missing values

2026-01-22T17:07:33.187140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-22T17:07:33.408497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

satisfaction_employee_environnementnote_evaluation_precedentesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_persoheure_supplementairesaugementation_salaire_precedentegenrerevenu_mensuelstatut_maritalpostea_quitte_l_entreprisenombre_participation_peenb_formations_suiviesniveau_educationfrequence_deplacementRatio_Stagnation_entreprisematch_etudes_posteduree_moyenne_par_postehabite_loinratio_stagnation_postenouveau_responsableRatio_Gain_Effort
02341111115993CélibataireCadre Commercial1002Occasionnel0.00000000.88888900.57142902996.5
13224302305130Marié(e)Assistant de Direction0131Frequent0.10000015.00000000.63636405130.0
24232311502090CélibataireConsultant1032Occasionnel0.00000001.00000000.00000011045.0
34333311112909Marié(e)Assistant de Direction0034Frequent0.37500014.00000000.77777811454.5
41324301203468Marié(e)Consultant0131Occasionnel1.00000010.60000000.66666703468.0
54343201303068CélibataireConsultant0022Frequent0.42857118.00000000.87500003068.0
63411212012670Marié(e)Consultant0333Occasionnel0.00000012.40000000.00000011335.0
74332302202693Divorcé(e)Consultant0121Occasionnel0.00000010.50000010.00000012693.0
84232302109526CélibataireTech Lead0023Frequent0.111111110.00000010.70000009526.0
93332201305237Marié(e)Manager0233Occasionnel1.00000012.42857110.87500005237.0
satisfaction_employee_environnementnote_evaluation_precedentesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_persoheure_supplementairesaugementation_salaire_precedentegenrerevenu_mensuelstatut_maritalpostea_quitte_l_entreprisenombre_participation_peenb_formations_suiviesniveau_educationfrequence_deplacementRatio_Stagnation_entreprisematch_etudes_posteduree_moyenne_par_postehabite_loinratio_stagnation_postenouveau_responsableRatio_Gain_Effort
14604212101413785CélibataireAssistant de Direction0034Occasionnel0.00000012.50000010.66666703785.0
146142123113010854Divorcé(e)Cadre Commercial1133Occasionnel0.66666714.00000010.50000015427.0
146222412011112031Marié(e)Cadre Commercial0121Occasionnel0.450000121.00000010.428571012031.0
14632312301909936CélibataireTech Lead0023Aucun0.111111110.00000000.40000009936.0
14644234301812966CélibataireReprésentant Commercial0023Occasionnel0.00000005.00000000.40000012966.0
14653443301702571Marié(e)Consultant0132Frequent0.00000013.40000010.33333302571.0
14664211301509991Marié(e)Manager0151Occasionnel0.14285711.80000000.87500009991.0
14672422312006142Marié(e)Tech Lead0103Occasionnel0.00000013.00000000.28571403071.0
14684224201405390Marié(e)Cadre Commercial0033Frequent0.00000005.66666700.60000005390.0
14692431401204404Marié(e)Consultant0033Occasionnel0.25000012.00000000.60000004404.0